This study examines the fidelity of the Meteorological Research Institute (MRI) atmospheric general circulation model (AGCM), ensemble runs forced with observed sea surface temperature (SST), in simulating Indian summer monsoon rainfall (ISMR), and its interannual variation. Despite the simple ensemble mean (SEM) capturing essential features of climatological ISMR pattern and its extreme ISMR anomalies, it still shows certain systematic bias in simulating mean seasonal variation of rainfall over the Asia-Pacific region. Concurrently, the ISMR interannual variability throughout the analysis period is not adequately represented.
A bias-correction is applied to remove this bias by deriving weights for the member simulations for each Julian day separately at every grid point through multiple linear regression of their daily rainfall, against corresponding observation over a 23-year ‘training phase’ (out of the total 24-year analysis period). Thereafter, at every grid point, for each Julian day of the remaining 1-year ‘forecast phase’, the bias-removed ensemble mean (BREM) is computed as an optimal linear combination of weighted member simulations. In cross validation, each year in the analysis period is treated successively as the ‘forecast phase’, with the remaining 23 years included in the corresponding training phase. The methodology minimises the systematic bias in mean seasonal variation of rainfall, and BREM consequently improves upon SEM in simulating the mean ISMR pattern, and its interannual variability over the entire analysis period.
The skill of BREM in forecasting the ISMR, and its intraseasonal variability is validated, for the severe monsoon drought of 2002. The effective removal of climatological bias brings out the realistic precipitation response in BREM to fluctuations in SST boundary forcing. As a result, BREM captures the seasonal rainfall anomaly pattern of 2002, and markedly improves its intraseasonal evolution. Apart from the basic skill of the AGCM ensemble system, pronounced equatorial SST impact in modulating the monsoon circulation during 2002, played a seminal role in the success of the methodology. The analysis also underlines the importance of mean seasonal variation, not only for capturing ISMR climatology, and its interannual variation but for improving its intraseasonal variability as well. With the aid of realistic SST forecasts, this methodology has the application potential for dynamical prediction of ISMR.